Paper Citation Record · LEDGER

RadioDUN: A Physics-Inspired Deep Unfolding Network for Radio Map Estimation

As of 8 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 1 inbound Pith citation observation for arXiv:2506.08418.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2506.08418 v2

Coverage vector

measured 41 of 41 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T05:18:29.207837Z

measured 42 of 42 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-15T09:24:16.570453Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-05-15T09:25:21.943811Z

Reference resolution

41 of 41 outbound references displayed

verified exact0
verified fuzzy28
unresolved13
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 874b11fd-a2d9-4bc9-a477-42785539e57c · outbound

This paper cites Radio map estimation: A data-driven approach to spectrum cartography,.

RadioDUN: A Physics-Inspired Deep Unfolding Network for Radio Map Estimation Radio map estimation: A data-driven approach to spectrum cartography,

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-07T05:18:24.586265Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e931b715-74ee-4bac-b68f-c8c5568b593b · outbound

This paper cites 6g wireless communication systems: Applications, requirements, technolo- gies, challenges, and research directions,.

RadioDUN: A Physics-Inspired Deep Unfolding Network for Radio Map Estimation 6g wireless communication systems: Applications, requirements, technolo- gies, challenges, and research directions,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:18:36.398185Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation d1f9b16f-6a5b-4456-a11f-ab99466e05aa · outbound

This paper cites Theoretical analysis of the radio map estimation problem,.

RadioDUN: A Physics-Inspired Deep Unfolding Network for Radio Map Estimation Theoretical analysis of the radio map estimation problem,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:18:36.170548Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation c7c5ea55-d043-4faa-bbad-3112c8715ece · outbound

This paper cites Overview of the first pathloss radio map prediction challenge,.

RadioDUN: A Physics-Inspired Deep Unfolding Network for Radio Map Estimation Overview of the first pathloss radio map prediction challenge,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:18:35.840406Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 2c102c34-7ec4-4f7b-bde2-1315adb1cdf1 · outbound

This paper cites A survey of wireless path loss prediction and coverage mapping methods,.

RadioDUN: A Physics-Inspired Deep Unfolding Network for Radio Map Estimation A survey of wireless path loss prediction and coverage mapping methods,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:18:35.541700Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 061bcb11-a910-45d6-8a19-fcbb13981f3f · outbound

This paper cites Investigation of prediction accuracy, sensitivity, and parameter stability of large-scale propagation path loss models for 5g wireless communications,.

RadioDUN: A Physics-Inspired Deep Unfolding Network for Radio Map Estimation Investigation of prediction accuracy, sensitivity, and parameter stability of large-scale propagation path loss models for 5g wireless communications,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:18:35.282171Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation bfe38a2c-48db-4984-8a6f-0d04b063f5db · outbound

This paper cites Ray tracing for radio propagation modeling: Principles and applications,.

RadioDUN: A Physics-Inspired Deep Unfolding Network for Radio Map Estimation Ray tracing for radio propagation modeling: Principles and applications,

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T05:18:25.347716Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 63f84bc0-067b-490a-8813-73e99dcc70ee · outbound

This paper cites Finite-difference modeling of very-low-frequency propagation in the earth-ionosphere waveguide,.

RadioDUN: A Physics-Inspired Deep Unfolding Network for Radio Map Estimation Finite-difference modeling of very-low-frequency propagation in the earth-ionosphere waveguide,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:18:34.964422Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation f487cd9d-a879-4086-8929-200e0debee98 · outbound

This paper cites A scalable and generalizable pathloss map prediction,.

RadioDUN: A Physics-Inspired Deep Unfolding Network for Radio Map Estimation A scalable and generalizable pathloss map prediction,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:18:34.760308Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation b2b8d371-870e-4142-adcd-3f1ebe180cdf · outbound

This paper cites Radiounet: Fast radio map estimation with convolutional neural networks,.

RadioDUN: A Physics-Inspired Deep Unfolding Network for Radio Map Estimation Radiounet: Fast radio map estimation with convolutional neural networks,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:18:34.600952Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation fbdf276f-7fe0-4e7f-bc2d-475c4980a9c8 · outbound

This paper cites Rme-gan: A learning framework for radio map estimation based on conditional generative adversarial network,.

RadioDUN: A Physics-Inspired Deep Unfolding Network for Radio Map Estimation Rme-gan: A learning framework for radio map estimation based on conditional generative adversarial network,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:18:34.295264Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation df104d37-5ae7-4dfa-a7af-7c3a8f2e4613 · outbound

This paper cites Deep learning for visual understanding: A review,.

RadioDUN: A Physics-Inspired Deep Unfolding Network for Radio Map Estimation Deep learning for visual understanding: A review,

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T05:18:25.843890Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a1497d64-04ed-468f-9840-e52d4a80f373 · outbound

This paper cites A survey of the usages of deep learning for natural language processing,.

RadioDUN: A Physics-Inspired Deep Unfolding Network for Radio Map Estimation A survey of the usages of deep learning for natural language processing,

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T05:18:25.988691Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 217a4471-be53-46aa-bab5-7bad7082b850 · outbound

This paper cites Generative ai on spectrumnet: An open benchmark of multiband 3d radio maps,.

RadioDUN: A Physics-Inspired Deep Unfolding Network for Radio Map Estimation Generative ai on spectrumnet: An open benchmark of multiband 3d radio maps,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:18:34.115931Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 8dac3233-db6c-4491-8f2c-c9f4cfd7f469 · outbound

This paper cites D 3 c 2-net: Dual-domain deep convolutional coding network for compressive sensing,.

RadioDUN: A Physics-Inspired Deep Unfolding Network for Radio Map Estimation D 3 c 2-net: Dual-domain deep convolutional coding network for compressive sensing,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:18:33.941500Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 88559df7-e523-4712-becf-03bc872029d6 · outbound

This paper cites Sparse bayesian learning-based 3d radio environment map construction—sampling optimization, scenario-dependent dictio- nary construction and sparse recovery,.

RadioDUN: A Physics-Inspired Deep Unfolding Network for Radio Map Estimation Sparse bayesian learning-based 3d radio environment map construction—sampling optimization, scenario-dependent dictio- nary construction and sparse recovery,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:18:33.588209Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 221289fc-cc77-4318-89f4-8e4165a104ff · outbound

This paper cites Sparse bayesian learning-based hierarchical construction for 3d radio environment maps incorporating channel shadowing,.

RadioDUN: A Physics-Inspired Deep Unfolding Network for Radio Map Estimation Sparse bayesian learning-based hierarchical construction for 3d radio environment maps incorporating channel shadowing,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:18:33.309695Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation ebc6d27e-bd63-4be1-9c98-27fed00ad55a · outbound

This paper cites Ista-net: Interpretable optimization-inspired deep network for image compressive sensing,.

RadioDUN: A Physics-Inspired Deep Unfolding Network for Radio Map Estimation Ista-net: Interpretable optimization-inspired deep network for image compressive sensing,

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-07T05:18:26.706690Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1972caff-1631-4fd8-af21-70306776cff7 · outbound

This paper cites Application of compressive sensing in cognitive radio communications: A survey,.

RadioDUN: A Physics-Inspired Deep Unfolding Network for Radio Map Estimation Application of compressive sensing in cognitive radio communications: A survey,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:18:32.942772Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 8c41a5e5-618c-4384-ada5-b1d647e96603 · outbound

This paper cites Progressive content-aware coded hyperspectral snapshot compressive imaging,.

RadioDUN: A Physics-Inspired Deep Unfolding Network for Radio Map Estimation Progressive content-aware coded hyperspectral snapshot compressive imaging,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:18:32.688698Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 5e4e410a-8c95-47b0-89e9-2f6d9a977213 · outbound

This paper cites Puert: Probabilistic under- sampling and explicable reconstruction network for cs-mri,.

RadioDUN: A Physics-Inspired Deep Unfolding Network for Radio Map Estimation Puert: Probabilistic under- sampling and explicable reconstruction network for cs-mri,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:18:32.512398Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation b9933c02-e726-4d06-af4c-6ad83772af61 · outbound

This paper cites Convergence of alternating opti- mization,.

RadioDUN: A Physics-Inspired Deep Unfolding Network for Radio Map Estimation Convergence of alternating opti- mization,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:18:32.158913Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation e6a80db2-72f3-40f3-8a29-a454dcbbcca7 · outbound

This paper cites Multi-Scale Context Aggregation by Dilated Convolutions.

RadioDUN: A Physics-Inspired Deep Unfolding Network for Radio Map Estimation Multi-Scale Context Aggregation by Dilated Convolutions

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-07T05:18:27.389910Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 21121749-fa33-4781-b874-fa3be7d2e6ac · outbound

This paper cites Physics-inspired machine learning for radiomap estimation: Integration of radio propagation mod- els and artificial intelligence,.

RadioDUN: A Physics-Inspired Deep Unfolding Network for Radio Map Estimation Physics-inspired machine learning for radiomap estimation: Integration of radio propagation mod- els and artificial intelligence,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:18:31.935974Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 21844820-1083-47dc-af0d-c98a6fbcfa90 · outbound

This paper cites Optimization-inspired cross-attention transformer for compressive sensing,.

RadioDUN: A Physics-Inspired Deep Unfolding Network for Radio Map Estimation Optimization-inspired cross-attention transformer for compressive sensing,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:18:31.670713Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 48a54d41-dcba-411e-bc9e-c368f0aa55d2 · outbound

This paper cites Cpp-net: Embracing multi-scale feature fusion into deep unfolding cp-ppa network for compressive sensing,.

RadioDUN: A Physics-Inspired Deep Unfolding Network for Radio Map Estimation Cpp-net: Embracing multi-scale feature fusion into deep unfolding cp-ppa network for compressive sensing,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:18:31.407659Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 3a97e89a-8822-4b14-a94e-ec4c62a28b39 · outbound

This paper cites Physics-inspired com- pressive sensing: Beyond deep unrolling,.

RadioDUN: A Physics-Inspired Deep Unfolding Network for Radio Map Estimation Physics-inspired com- pressive sensing: Beyond deep unrolling,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:18:31.179759Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation a8d99eb5-676b-485e-8b68-cba007040e19 · outbound

This paper cites Video-rate hyperspectral camera based on a cmos-compatible random array of fabry–p´erot filters,.

RadioDUN: A Physics-Inspired Deep Unfolding Network for Radio Map Estimation Video-rate hyperspectral camera based on a cmos-compatible random array of fabry–p´erot filters,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:18:30.952012Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 2ec436a7-7754-4b35-a29c-46dbd8aa70a2 · outbound

This paper cites Propagation path loss models for 5g urban micro-and macro-cellular scenarios,.

RadioDUN: A Physics-Inspired Deep Unfolding Network for Radio Map Estimation Propagation path loss models for 5g urban micro-and macro-cellular scenarios,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:18:30.777380Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation edcbf6a8-da58-46ca-bccf-bd0e3e52282c · outbound

This paper cites an unresolved cited work.

RadioDUN: A Physics-Inspired Deep Unfolding Network for Radio Map Estimation Unresolved cited work

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-07T05:18:28.056331Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b98ba189-dbf0-46e8-83e5-cc35973ff1bb · outbound

This paper cites Mask-guided spectral-wise transformer for efficient hyperspectral image reconstruction,.

RadioDUN: A Physics-Inspired Deep Unfolding Network for Radio Map Estimation Mask-guided spectral-wise transformer for efficient hyperspectral image reconstruction,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:18:30.500436Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation bdb4a155-5e03-470d-a6c7-e551fb5c0242 · outbound

This paper cites Ista-net++: Flexible deep unfolding net- work for compressive sensing,.

RadioDUN: A Physics-Inspired Deep Unfolding Network for Radio Map Estimation Ista-net++: Flexible deep unfolding net- work for compressive sensing,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:18:30.242067Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 6e08f819-2d97-4bd8-b16e-7e218acbe0b7 · outbound

This paper cites Cbam: Convolutional block attention module,.

RadioDUN: A Physics-Inspired Deep Unfolding Network for Radio Map Estimation Cbam: Convolutional block attention module,

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-07T05:18:28.413960Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8362c92e-ce27-44f7-8fdc-cddbf7532b8f · outbound

This paper cites Xception: Deep learning with depthwise separable convolu- tions,.

RadioDUN: A Physics-Inspired Deep Unfolding Network for Radio Map Estimation Xception: Deep learning with depthwise separable convolu- tions,

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-07T05:18:28.482016Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b79b62d8-906d-43fb-a9b5-0571a013ec8b · outbound

This paper cites Plug-and-play admm for image restoration: Fixed-point convergence and applications,.

RadioDUN: A Physics-Inspired Deep Unfolding Network for Radio Map Estimation Plug-and-play admm for image restoration: Fixed-point convergence and applications,

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-07T05:18:28.604464Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a977a9f6-1368-4278-b699-47ca445f14ea · outbound

This paper cites U-net: Convolutional networks for biomedical image segmentation,.

RadioDUN: A Physics-Inspired Deep Unfolding Network for Radio Map Estimation U-net: Convolutional networks for biomedical image segmentation,

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-07T05:18:28.727998Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 45db501b-e4cc-49b1-a568-372ac0ee91b8 · outbound

This paper cites Hybrid cnn-transformer ar- chitecture for efficient large-scale video snapshot compressive imaging,.

RadioDUN: A Physics-Inspired Deep Unfolding Network for Radio Map Estimation Hybrid cnn-transformer ar- chitecture for efficient large-scale video snapshot compressive imaging,

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-07T05:18:28.803635Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b2bd4600-26da-4523-88bd-ae7952b452ec · outbound

This paper cites Dominant path prediction model for urban scenarios,.

RadioDUN: A Physics-Inspired Deep Unfolding Network for Radio Map Estimation Dominant path prediction model for urban scenarios,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:18:30.011764Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 9fbaa336-7266-4261-88e0-f1ec7a3abf12 · outbound

This paper cites Verifying path loss and delay spread predictions of a 3d ray tracing propagation model in urban environment,.

RadioDUN: A Physics-Inspired Deep Unfolding Network for Radio Map Estimation Verifying path loss and delay spread predictions of a 3d ray tracing propagation model in urban environment,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:18:29.789493Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation d487e8ef-58ac-4dd8-949d-ce97a75ad3f1 · outbound

This paper cites Mtc-csnet: Marrying transformer and convolution for image compressed sensing,.

RadioDUN: A Physics-Inspired Deep Unfolding Network for Radio Map Estimation Mtc-csnet: Marrying transformer and convolution for image compressed sensing,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:18:29.459743Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 505d5e59-868c-40ed-b138-30eb9c5b81c6 · outbound

This paper cites A comprehensive survey on transfer learning,.

RadioDUN: A Physics-Inspired Deep Unfolding Network for Radio Map Estimation A comprehensive survey on transfer learning,

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-07T05:18:29.207837Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 37b34e47-aa77-4b4b-affa-77c854a6e9e6 · inbound

A Tutorial on Learning-Based Radio Map Construction: Data, Paradigms, and Physics-Awareness cites this paper.

A Tutorial on Learning-Based Radio Map Construction: Data, Paradigms, and Physics-Awareness RadioDUN: A Physics-Inspired Deep Unfolding Network for Radio Map Estimation

Reference 59

Resolution: verified exact; arxiv_id, observed 2026-05-15T09:25:21.947752Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.